Genetic algorithms and investment strategies: A global perspective

The profitability of momentum portfolios in the equity markets is derived from the continuation of stock returns over medium time horizons. The empirical evidence of momentum, however, is significantly different across markets around the world. The purpose of this dissertation is to: (1) help global investors determine the optimal selection and holding periods for momentum portfolios, (2) evaluate the profitability of the optimized momentum portfolios in different time periods and market states, (3) assess the investment strategy profits after considering transaction costs, and (4) interpret momentum returns within the framework of prior studies on investors’ behavior. Improving on the traditional practice of selecting arbitrary selection and holding periods, a genetic algorithm (GA) is employed. The GA performs a thorough and structured search to capture the return continuations and reversals patterns of momentum portfolios. Three portfolio formation methods are used: price momentum, earnings momentum, and earnings and price momentum and a non-linear optimization procedure (GA). The focus is on common equity of the U.S. and a select number of countries, including Australia, France, Germany, Japan, the Netherlands, Sweden, Switzerland and the United Kingdom. The findings suggest that the evolutionary algorithm increases the annualized profits of the U.S. momentum portfolios. However, the difference in mean returns is statistically significant only in certain cases. In addition, after considering transaction costs, both price and earnings and price momentum portfolios do not appear to generate abnormal returns. Positive risk-adjusted returns net of trading costs are documented solely during “up” markets for a portfolio long in prior winners only. The results on the international momentum effects indicate that the GA improves the momentum returns by 2 to 5% on an annual basis. In addition, the relation between momentum returns and exchange rate appreciation/depreciation is examined. The currency appreciation does not appear to influence significantly momentum profits. Further, the influence of the market state on momentum returns is not uniform across the countries considered. The implications of the above findings are discussed with a focus on the practical aspects of momentum investing, both in the U.S. and globally.

Genetic Algorithms and Investment Strategies: A Global Perspective

The profitability of momentum portfolios in the equity markets is derived from the continuation of stock returns over medium time horizons. The empirical evidence of momentum, however, is significantly different across markets around the world. The purpose of this dissertation is to: 1) help global investors determine the optimal selection and holding periods for momentum portfolios, 2) evaluate the profitability of the optimized momentum portfolios in different time periods and market states, 3) assess the investment strategy profits after considering transaction costs, and 4) interpret momentum returns within the framework of prior studies on investors’ behavior. Improving on the traditional practice of selecting arbitrary selection and holding periods, a genetic algorithm (GA) is employed. The GA performs a thorough and structured search to capture the return continuations and reversals patterns of momentum portfolios. Three portfolio formation methods are used: price momentum, earnings momentum, and earnings and price momentum and a non-linear optimization procedure (GA). The focus is on common equity of the U.S. and a select number of countries, including Australia, France, Germany, Japan, the Netherlands, Sweden, Switzerland and the United Kingdom. The findings suggest that the evolutionary algorithm increases the annualized profits of the U.S. momentum portfolios. However, the difference in mean returns is statistically significant only in certain cases. In addition, after considering transaction costs, both price and earnings and price momentum portfolios do not appear to generate abnormal returns. Positive risk-adjusted returns net of trading costs are documented solely during “up” markets for a portfolio long in prior winners only. The results on the international momentum effects indicate that the GA improves the momentum returns by 2 to 5% on an annual basis. In addition, the relation between momentum returns and exchange rate appreciation/depreciation is examined. The currency appreciation does not appear to influence significantly momentum profits. Further, the influence of the market state on momentum returns is not uniform across the countries considered. The implications of the above findings are discussed with a focus on the practical aspects of momentum investing, both in the U.S. and globally.

A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.

Optimization of Cooling Protocols for Hearts Destined for Transplantation

Design and analysis of conceptually different cooling systems for the human heart preservation are numerically investigated. A heart cooling container with required connections was designed for a normal size human heart. A three-dimensional, high resolution human heart geometric model obtained from CT-angio data was used for simulations. Nine different cooling designs are introduced in this research. The first cooling design (Case 1) used a cooling gelatin only outside of the heart. In the second cooling design (Case 2), the internal parts of the heart were cooled via pumping a cooling liquid inside both the heart’s pulmonary and systemic circulation systems. An unsteady conjugate heat transfer analysis is performed to simulate the temperature field variations within the heart during the cooling process. Case 3 simulated the currently used cooling method in which the coolant is stagnant. Case 4 was a combination of Case 1 and Case 2. A linear thermoelasticity analysis was performed to assess the stresses applied on the heart during the cooling process. In Cases 5 through 9, the coolant solution was used for both internal and external cooling. For external circulation in Case 5 and Case 6, two inlets and two outlets were designed on the walls of the cooling container. Case 5 used laminar flows for coolant circulations inside and outside of the heart. Effects of turbulent flow on cooling of the heart were studied in Case 6. In Case 7, an additional inlet was designed on the cooling container wall to create a jet impinging the hot region of the heart’s wall. Unsteady periodic inlet velocities were applied in Case 8 and Case 9. The average temperature of the heart in Case 5 was +5.0oC after 1500 s of cooling. Multi-objective constrained optimization was performed for Case 5. Inlet velocities for two internal and one external coolant circulations were the three design variables for optimization. Minimizing the average temperature of the heart, wall shear stress and total volumetric flow rates were the three objectives. The only constraint was to keep von Mises stress below the ultimate tensile stress of the heart’s tissue.